wikgithub/crystalgan

Learning to Discover Crystallographic Structures with Generative Adversarial Networks

CrystalGAN

Learning to Discover Crystallographic Structures with Generative Adversarial Networks

Description

This repository is a TensorFlow implementation of CrystalGAN : CrystalGAN: Learning to Discover Crystallographic Structures with Generative Adversarial Networks. AAAI Spring Symposium: Combining Machine Learning with Knowledge Engineering 2019

Requirements

• Python 2.7
• Jupyter
• Tensorflow-gpu
• Scikit-learn
• Tqdm
• Pymatgen
• Matplotlib
• Numpy/Scipy

Note: Experiments must be run using GPU with powerful graphics card.

Usage

Clone the repository with:

git clone https://github.com/asmanouira/CrystalGAN 

Then:

cd CrystalGAN/

CrystalGAN is based on three steps:

• First and second steps are implemented in CrystalGAN_step1+2.py
• Third and last step is implemented in CrystalGAN_step3.py

Launch jupyter notebook:

jupyter notebook

• Open Step1+Step2_CrystalGAN.ipynb

Datasets

CrystalGAN takes as input two datasets of binary compounds and generates as output ternary compounds. We choose in this implementation as example Pd-H "Palladium - Hydrogen" and Ni-H "Nickel - Hydrogen", The aim is to generate novel ternary compounds of Pd-H-Ni "Palladium - Hydrogen - Nickel"

Samples in our datasets are POSCAR files and converted to 4D tensors as shown below:

In order to prepare the inputs for complexity augmentation , we add an empty placeholder for each dataset:

This procedure described above was implemented in Matlab: POSCAR2mat.m

Networks

CrystalGAN is composed basically of two cross-domain GANs.

Each encoder and decoder of the generators and the discriminators are composed of fully-connected layers.

The output datasets of the first network (including STEP1 and STEP2) will be trained by the second cross-domain GAN

To check the architecture of CrystalGAN network, we can use tensorboard:

tensorboard --logdir=graphs/

Results

CrystalGAN generates ternary compounds in 4D tensors and then print them as POSCAR files. We evaluate the generated crystal structures by:

• Visualizing the lattice of the crystal VESTA using the generated POSCAR files.
• Visualizing their distances histogram of first neighbors for all atoms in the cell.
• Check if the first neighbors distances respect the reinforced constraints by printing them in tables

To compute neighbors of all atoms in a crystallographic structure using POSCAR file as input argument: see neighbors.py An example of a POSCAR file is in data/.

In our study, the penalized first neighbors distances are between the atoms Pd-Pd', Ni-Ni', Pd-Ni and H-H'.
Those distances fixed to be between d1 = 1.8 Å and d2 = 3 Å.